Construction and Analysis of Disulfidptosis Gene Diagnosis Model for Osteosarcoma Based on Machine Learning
10.11969/j.issn.1673-548X.2024.08.013
- VernacularTitle:基于机器学习的骨肉瘤双硫死亡基因诊断模型建立与分析
- Author:
Weicai LI
1
;
Gang QIN
;
Kaiyi HE
Author Information
1. 530000 南宁,广西中医药大学研究生院
- Keywords:
Osteosarcoma;
Disulfidptosis;
Diagnostic models;
Machine learning
- From:
Journal of Medical Research
2024;53(8):62-68
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify the characteristic genes associated with osteosarcoma(OS)disulfidptosis by machine learning al-gorithm,and to construct a diagnostic prediction model,so as to provide theoretical support for further exploring the potential biomarkers and molecular mechanisms of early diagnosis of OS.Methods Differential expression analysis was used to identify OS differential expres-sion disulfidptosis-related genes(DE-DRG).The least absolute shrinkage and selection operator(LASSO),support vector machines(SVM)and random forest(RF)algorithms were used to further identify the diagnostically valuable OS disulfidptosis characteristic genes,and evaluated their diagnostic value by plotting the receiver operating characteristic(ROC)curve.At the same time,a nomogram was constructed to assess the risk of disease,and the effective performance of the nomogram was evaluated by calibration curve and clinical de-cision curve.The expression of characteristic genes in OS tissues was detected by real-time quantitative polymerase chain reaction(RT-qPCR).Results A total of two genes(NDUFA11,RPN1)were identified with high diagnostic value of characteristic genes asso-ciated with osteosarcoma(OS)disulfidptosis,and the nomogram constructed had high reliability for predicting disease risk.The results of RT-qPCR showed that NDUFA11 expression was significantly reduced,while RPN1 was significantly increased in OS tissue(P＜0.01).Conclusion The established genetic diagnostic model of OS disulfidptosis in this study has certain diagnostic value.
